Floods Data Pathfinder

Two days of heavy rain, May 2020, led to catastrophic dam failures and major flooding in Midland County, Michigan. The natural-color image is from Landsat's Operational Land Imager.

Heavy rain in May 2020 led to catastrophic dam failures and major flooding in Midland County, Michigan. Natural-color image from Landsat's Operational Land Imager. Credit: NASA Earth Observatory

Floods affect more people than any other type of natural disaster and the number of flood-related disasters is increasing at an alarming rate, resulting in major social and economic losses. The United Nations, in its Sustainable Development Goals, has called for a transition from just responding to flood events to actually assessing the vulnerability, exposure, and risk of communities with the ultimate goal of mitigating potential devastating impacts.

Scientists and decision makers, using satellite- and ground-based data as well as models, can evaluate the factors which contribute to an area's predisposition to flooding, including precipitation, soil moisture, and topography. They can use satellite data to monitor inundation and respond to events in near real-time.




Find Pre-Event Data

Find Pre-Event Data

Precipitation

NASA's Precipitation Measurement Mission (PMM) provides a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) missions. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

Screenshot of IMERG data in Earthdata Search

The products from GPM are available individually and have also been integrated with data from a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes. The Integrated Multi-satellitE Retrievals for GPM (IMERG) mission contains multiple runs, which accommodate different user requirements for latency and accuracy (Early = 4 hours, e.g., for flooding events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research).

Research-quality data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late, and Final precipitation data on the half hour or 1-day timeframe. Data are in NetCDF or HDF format, and can be opened using Panoply. Data are available from 2000.

Geographic Information System (GIS) analysis-ready datasets in GeoTIFF format can be accessed from the Precipitation Processing System website. Note that you will need to register to access the FTP site.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Data can be visualized in Worldview:

Daymet is a collection of gridded estimates of daily weather parameters. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved in a variety of ways, including Earthdata Search, an Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) API, and tools developed by ORNL DAAC, or through NASA's Land Processes DAAC (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS).

Soil Moisture

Soil moisture is important for surface hydrology as it controls the amount of water that can infiltrate into the ground, replenishing our aquifers or contributing to excess runoff.

Current ground measurements of soil moisture are sparse and have limited coverage; satellite data help fill in those gaps. On the other hand, satellite data are limited by their relatively coarse resolution; the preferred measurement should be chosen based upon your needs. Utilizing a combination of both ground-based and remote sensing data provides for spatial and temporal data continuity.

NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top five cm of soil globally, every 2–3 days, at a resolution of 9–36 km. NASA, in collaboration with other agencies, has also developed models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates of soil moisture and evapotranspiration.

Research-quality data products can be accessed via Earthdata Search; datasets are available as HDF5 (SMAP) files which are also customizable to GeoTIFF.

Soil moisture as visualized with ORNL DAAC Soil Moisture Visualizer. The map shows a flight path over Arizona in 2013. In the graph, AirMoss rootzone soil moisture data is plotted with SMAP rootzone soil moisture. Root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth.

Soil moisture as visualized with ORNL DAAC Soil Moisture Visualizer. The map shows a flight path over Arizona in 2013. In the graph, AirMoss rootzone soil moisture data is plotted with SMAP rootzone soil moisture. Root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth.

ORNL DAAC's Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. LP DAAC's AppEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products. See Tools for Data Access and Visualization for additional information.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Data can be visualized in Worldview:

Snow Cover and Snow Water Equivalent

Snow cover is the presence of snow on land or bodies of water; measurements are acquired during the daytime and under cloud-clear conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites measure snow cover. Snow Water Equivalent (SWE) is the amount of water contained in snowpack in the Northern and Southern Hemispheres measured in millimeters (mm). It is analogous to melting the snow and measuring the depth of the resulting pool of water. SWE measurements are useful for assessing both the potential surface runoff when the snow melts and the water availability for regions in lower elevations. The MODIS instrument measures snow cover while the Advanced Microwave Scanning Radiometer (AMSR) measures snow water equivalent.

Research-quality data products can be accessed via Earthdata Search; datasets are available as HDF files, which can be opened using Panoply.

Data can be visualized in Worldview:

LP DAAC's AppEEARS offers another option to simply and efficiently extract subsets, transform, visualize, and download MODIS Snow Cover data products.

Runoff

Runoff after storm events can impact the amount of water entering a channel or water body. Satellites cannot measure runoff directly but information that can be used to assess predicted runoff can be measured using remote sensing. These data are input, along with ground-based data, into atmosphere-land models from LDAS to estimate runoff.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Topography

Knowing local topography is essential for disaster managers and emergency management professionals seeking to assess an area's risk level; knowing the height at which communities sit in relation to flood waters determines the exposure.

An ASTER GDEM image of Mt. Raung and the surrounding area.

An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

A method for delineating topography is NASA's Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover, however. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc-second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

NASA's Socioeconomic Data and Applications Center (SEDAC) provides a number of datasets on population exposure and vulnerability and flood hazard potential.

NASA Disasters Flood Dashboard

The dashboard contains experimental NASA products that may assist in preparing for or responding to a tropical cyclone. Datasets include precipitation, sea surface temperature, radar reflectivity, flood detection, soil moisture, and wind speed.

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Find Flood Inundation Data

Find Flood Inundation Data

Rains from ex-tropical Cyclone Esther bring floodwaters to South West Queensland, Australia
Rains from ex-tropical Cyclone Esther bring floodwaters to South West Queensland, Australia, March 2020.

NASA has several products that can be used to assess flooding qualitatively: Landsat, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (aboard the Terra and Aqua satellites), and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NASA/NOAA Joint Polar Satellite System NOAA-20 satellites. However, these sensors are impeded by cloud cover and nighttime conditions. During a storm, this is a significant drawback. Once the storm has passed, with a pre-event image and a post-event image, a more quantitative assessment of flooding extent can be made.

During the storm, Synthetic Aperture Radar (SAR) may provide a better "view," as it is able to penetrate cloud cover and works in both day and night conditions. SAR data are more complex, requiring a certain level of processing skill. See Tools for Data Access and Visualization for information on processing Sentinel SAR data.

Research-quality (higher-level "standard") data products can be accessed via Earthdata Search or through NASA partner websites:

  • MODIS Surface Reflectance from Earthdata Search
    Note that a false color image created by combining bands 7 as red, 2 as green, and 1 as blue is useful for enhancing flood conditions. In a false color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section.
  • Suomi NPP VIIRS Surface Reflectance from Earthdata Search
    Note that a false color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section.
  • Landsat Data from USGS Earth Explorer
    Landsat is a joint NASA/USGS program that provides the longest continuous space-based record of Earth's land in existence. On the Earth Explorer site specify your search criteria, then:
    1. Select "Data Sets"
    2. Select Landsat
    3. Select Landsat Collection 1 Level-1
    4. Select Landsat 7 and/or Landsat 8
    These files can be downloaded as Level-1 GeoTIFF Data Products. Note that you will need a USGS login to proceed.

Map of inundation in Amazon rain forest. The color key provides the color for the number of times an area was flooded, and 18 represents constant river. Numbers less than 18 are the number of times an area floods. Credit: Anderson 2017; RTC: ASF 2015; Includes Material © JAXA/METI 2007

Map of inundation in Amazon rain forest. The color key provides the color for the number of times an area was flooded, and 18 represents constant river. Numbers less than 18 are the number of times an area floods. Credit: ASF DAAC; Includes Material © JAXA/METI 2007

Synthetic Aperture Radar

SAR is a type of active data collection where a sensor produces its own energy and then records the amount of that energy reflected back after interacting with the Earth. While optical imagery is similar to interpreting a photograph, SAR data require a different way of thinking in that the signal is instead responsive to surface characteristics like structure and moisture.

  • Sentinel-1A and Sentinel-1B SAR data from Earthdata Search
    There are several options when choosing SAR data. Polarization is the direction the signal is transmitted and/or received. Dual polarization, for example, refers to two different signal directions. Another option for SAR data is the inclusion of phase information. Level 1 data are produced as single look complex (SLC), in which the phase information is preserved, or as ground-range detected (GRD), in which the phase information is lost. Dual polarized GRD is acceptable for flood inundation mapping.
  • Sentinel-1A and Sentinel-1B SAR data from Vertex
    Vertex is NASA's Alaska Satellite Facility DAAC (ASF DAAC) search tool, which allows for the preview of numerous types of SAR data, including Sentinel-1.

To learn more about SAR and processing Level 1 data, view the SAR-related Earthdata webinars, NASA's Applied Remote Sensing Training (ARSET) Program Introduction to SAR training, or ASF DAAC's inundation data recipes.

Near Real-time Data

Data (often in NRT) can be visualized in Worldview and via NASA's experimental Global Flood Mapping and Advanced Rapid Imaging and Analysis (ARIA) projects:

  • Suomi NPP VIIRS Corrected Reflectance in Worldview
    Note that a false color image created by combining bands M11 as red, I2 as green, and I1 as blue, or M3-I3-M11, is useful for enhancing flood conditions. In a false color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges.
  • NOAA-20 VIIRS Corrected Reflectance in Worldview
    Note that a false color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges.
  • MODIS Flood Inundation Maps
    1-3 or 14 day composites; flood water and surface water layers can be downloaded as shapefiles, while the water product itself can be downloaded as a GeoTIFF.
  • Event Response to Floods at ARIA
    The ARIA Project, a joint effort of the California Institute of Technology and NASA's Jet Propulsion Laboratory, is developing the infrastructure to generate imaging products in near real-time that can improve situational awareness for disaster response. Products are in folders based on the date of the event and the location.

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Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite | Spatial Data Access Tool (SDAT)| Sentinel Toolbox

Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the Earth science disciplines. Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections.

Screenshot of the Search Earthdata site.

In the project area, for some datasets, you can customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML, or a GeoTIFF. You can also choose from a variety of projection options. Lastly, you can subset the data, obtaining only the bands that are needed.

Earthdata Search customization tools diagram.

Panoply

HDF and NetCDF files can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.

Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are the more popular ones.

  • Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  • Map animations are a means to observe spatial patterns and detect unusual events over time.
  • Area-averaged time series are used to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step.
  • Histogram plots are used to display the distribution of values of a data variable in a selected region and time interval.

For more detailed tutorials:

  • Giovanni How-To's on NASA's GES DISC YouTube channel.
  • Data recipe for downloading a Giovanni map as NetCDF and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text.

Worldview

NASA's EOSDIS Worldview visualization application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview now includes nine geostationary imagery layers from GOES-East, GOES-West and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the visualization of the differentiation between air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. The post-hurricane image on the left shows widespread outages around San Juan, including key hospital and transportation infrastructure.

AppEEARS

AppEEARS, from LP DAAC, offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons.

Performing Area Extractions

After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you will specify a series of parameters that are used to extract data for your area(s) of interest.

Spatial Subsetting

Define your region of interest in one of these three ways:

  • Upload a vector polygon file in shapefile format (you can upload a single file with multiple features or multipart single features). The .shp, .shx, .dbf, or .prj files must be zipped into a file folder to upload.
  • Upload a vector polygon file in GeoJSON format (can upload a single file with multiple features or multipart single features).
  • Draw a polygon on the map by clicking on the Bounding box or Polygon icons (single feature only).

Select the date range for your time period of interest.

Specify the range of dates for which you wish to extract data by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting dates a start and end date in the calendar.

Adding Data Layers

Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching your query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.

​Extracting an area in AppEEARS for flood assessment

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF-4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF-4 is selected, outputs will be grouped into .nc files by product and by feature.

If GeoTIFF is selected, you must select a projection

Interacting with Results

Once your request is completed, from the Explore Requests page, click the View icon in order to view and interact with your results. This will take you to the View Area Sample page.

The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AppEEARS ID (AID). If your feature contains attribute table information, you can view the feature attribute table data by clicking on the Information icon to the right of the Feature dropdown. To view statistics from different features or layers, select a different AID from the Feature dropdown and/or a different layer of interest from the Layer dropdown.

Interpreting Results in AppEEARS

Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF-4 files.

Soil Moisture Visualizer

ORNL DAAC has developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of different soil moisture datasets over North America. The visualization tool incorporates in-situ, airborne, and remote sensing data into one easy-to-use platform. This integration helps to validate and calibrate the data, and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide.

To use the visualizer, select a dataset of interest under Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and relative time frames of data collection. Upon selection of the parameter, the tool displays a time series with available datasets. All measurements are volumetric soil moisture. Surface soil moisture is the daily average of measurements at 0-5 cm depth, and root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth. Lastly it provides data sources for download.

ORNL DAAC Soil Moisture Visualizer

The Soil Moisture Visualizer allows users to compare soil moisture measurements from multiple sources (figure legends, top left and bottom right) at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data from NASA’s Soil Moisture Active Passive (SMAP) Observatory are shown with data from in situ sensors across the 9-kilometer Equal-Area Scalable Earth (EASE) grid cell encompassing the Tonzi Ranch Fluxnet site in the Sierra Nevada foothills of California. Daily precipitation values for the site (purple spikes) are also provided for reference.

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several MODIS and VIIRS Subset Tools for subsetting data.

  • With the Global Subset Tool, you can request a subset for any location on earth, provided as GeoTIFF and in text format, including interactive time-series plots and more. Users specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, you can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. You will need an Earthdata Login to request data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 3000+ field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as NEON, ForestGeo, PhenoCam and LTER that are of relevance to the biodiversity community.
  • With the Web Service, you can retrieve subset data (in real-time) for any location(s), time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into users' workflow.

Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

Spatial Data Access Tool (SDAT)

ORNL DAAC's SDAT is an Open Geospatial Consortium standards-based web application to visualize and download spatial data in various user-selected spatial/temporal extents, file formats, and projections. Several datasets including land cover, biophysical properties, elevation, and selected ORNL DAAC archived data are available through SDAT. KMZ files are also provided for data visualization in Google Earth.

Within SDAT, select a dataset of interest. Upon selection, the map service will open displaying the various measurements, with the associated granule, and a visualization of the selected granule.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

You can then select your spatial extent, projection, and output format for downloading.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

Sentinel Toolbox

The European Space Agency (ESA) Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with SAR instruments operating at a C-Band frequency. They orbit 180° apart, together imaging the entire Earth every six days. SAR is an active sensor and so can penetrate cloud cover and vegetation canopy and can observe at night. Therefore, it is ideal for flood inundation mapping. It also provides useful information to detect movement of Earth material after an earthquake, volcanic eruption or landslide. SAR data are very complex to process, however, ESA has developed a Sentinel-1 Toolbox to aid with processing and analysis of Sentinel-1 data.

For more information on active sensors, see What is Remote Sensing? and for more information on SAR specifically, see What is SAR?.

Flood Inundation Mapping

SAR signal penetration by sensor wavelength λ

SAR signal penetration by sensor wavelength λ. Credit: NASA SAR Handbook.

Before choosing data, it's important to determine which band meets your needs, as radar signals penetrate deeper as the sensor wavelength increases. This difference in penetration is due to the dielectric properties of a given medium, which dictate how much of the incoming radiation scatters at the surface, how much signal penetrates into the medium, and how much of the energy gets lost to the medium through absorption.

Another important parameter to take into consideration when choosing a dataset is the polarization, or the direction in which the signal is transmitted and/or received: horizontally or vertically. Dual polarization, for example, refers to two different signal directions, horizontal/vertical and vertical/horizontal (HV and VH). Knowing the polarization from which a SAR image was acquired is important, as signals at different polarizations interact differently with objects on the ground, affecting the recorded radar brightness in a specific polarization channel.

Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils).

Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils). Credit: NASA SAR Handbook.

Once you have downloaded the needed SAR data, it must be calibrated to account for distortion in the data. The objective in performing calibration is to create an image where the value of each pixel is directly related to the backscatter of the surface. So calibration takes into account radiometric distortion, signal loss as the wave propagates, saturation, and speckle. This process is critical for analyzing images quantitatively; it is also important for comparing images from different sensors, modalities, processors, and different acquisition dates.

Screenshot of the Sentinel-1 toolbox

Important note: DO NOT unzip the downloaded SAR file. Open the .zip file from within the Sentinel Toolbox. When you expand the Bands folder, you will see an amplitude and an intensity file for each polarization option. (The intensity band is a virtual one and is the square of the amplitude.) Open the amplitude file. Subset the data by zooming in to the area of interest and right-clicking on "Spatial Subset from View."

Calibration is done by following these steps:

  1. Radiometric calibration is performed by selecting Radar/Radiometric/Calibration (leave parameters as default).
  2. Geometric correction is done next to fix the main geometric distortions, due to Slant Range, Layover, Shadow, and Foreshortening. Terrain correction can be performed by selecting Radar/Geometric/Terrain Correction/ Range-Doppler Terrain Correction. This requires a digital elevation model (within the processing parameters, SRTM is the default selection). You can also specify a map projection in the processing parameters.

Sentinel-1 Toolbox Geometric Correction

Another characteristic of SAR images that must be accounted for is speckle. Speckle is the grey level variation that occurs between adjacent resolution cells, creating a grainy texture. Within the Toolbox, speckle can be removed by selecting "Radar/Speckle Filtering/Single Product Speckle Filter," and then choosing a type of filter; "Lee" is one of the most common.

Comparison of speckle in SAR imagery within Sentinel-1 Toolbox

For further information on SAR flood inundation mapping, see ASF DAAC's flood inundation recipes for QGIS or ArcGIS.

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Published September 18, 2020

Page Last Updated: Oct 21, 2020 at 9:33 PM EDT